NM01 – Machine Learning Assisted Material and Device Parameter Extraction From Measurements Of Thin Film Semiconductor Devices

The simulation of thin film semiconductor devices is challenging, partly due to the unknown material and device parameters. In this contribution, we present two different approaches to determine the missing material and device parameters from measurements. They both have in common that they are based on machine learning (ML) and numerical models. First, a numerical model describing the experiment is used to generate synthetic data to train a machine learning model the underlying material parameters. After successful training, a measurement is presented to the ML model to predict the parameters. In a more recent physics-informed ML approach, we integrate the model into the ML method and thus reduce the training data set.

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